Overview

Dataset statistics

Number of variables18
Number of observations105000
Missing cells8563
Missing cells (%)0.5%
Duplicate rows790
Duplicate rows (%)0.8%
Total size in memory14.4 MiB
Average record size in memory144.0 B

Variable types

Numeric16
Categorical2

Alerts

rerun has constant value "301.0" Constant
Dataset has 790 (0.8%) duplicate rowsDuplicates
objid is highly correlated with runHigh correlation
u is highly correlated with g and 3 other fieldsHigh correlation
g is highly correlated with u and 3 other fieldsHigh correlation
r is highly correlated with u and 3 other fieldsHigh correlation
i is highly correlated with u and 3 other fieldsHigh correlation
z is highly correlated with u and 3 other fieldsHigh correlation
run is highly correlated with objidHigh correlation
specobjid is highly correlated with plate and 1 other fieldsHigh correlation
plate is highly correlated with specobjid and 1 other fieldsHigh correlation
mjd is highly correlated with specobjid and 1 other fieldsHigh correlation
objid is highly correlated with runHigh correlation
u is highly correlated with g and 3 other fieldsHigh correlation
g is highly correlated with u and 3 other fieldsHigh correlation
r is highly correlated with u and 3 other fieldsHigh correlation
i is highly correlated with u and 3 other fieldsHigh correlation
z is highly correlated with u and 3 other fieldsHigh correlation
run is highly correlated with objidHigh correlation
specobjid is highly correlated with plate and 1 other fieldsHigh correlation
plate is highly correlated with specobjid and 1 other fieldsHigh correlation
mjd is highly correlated with specobjid and 1 other fieldsHigh correlation
objid is highly correlated with runHigh correlation
u is highly correlated with gHigh correlation
g is highly correlated with u and 3 other fieldsHigh correlation
r is highly correlated with g and 2 other fieldsHigh correlation
i is highly correlated with g and 2 other fieldsHigh correlation
z is highly correlated with g and 2 other fieldsHigh correlation
run is highly correlated with objidHigh correlation
specobjid is highly correlated with plate and 1 other fieldsHigh correlation
plate is highly correlated with specobjid and 1 other fieldsHigh correlation
mjd is highly correlated with specobjid and 1 other fieldsHigh correlation
rerun is highly correlated with classHigh correlation
class is highly correlated with rerunHigh correlation
objid is highly correlated with ra and 5 other fieldsHigh correlation
ra is highly correlated with objid and 3 other fieldsHigh correlation
dec is highly correlated with objid and 5 other fieldsHigh correlation
u is highly correlated with g and 3 other fieldsHigh correlation
g is highly correlated with u and 5 other fieldsHigh correlation
r is highly correlated with u and 5 other fieldsHigh correlation
i is highly correlated with u and 4 other fieldsHigh correlation
z is highly correlated with u and 4 other fieldsHigh correlation
run is highly correlated with objid and 5 other fieldsHigh correlation
field is highly correlated with raHigh correlation
specobjid is highly correlated with objid and 5 other fieldsHigh correlation
class is highly correlated with g and 7 other fieldsHigh correlation
redshift is highly correlated with g and 2 other fieldsHigh correlation
plate is highly correlated with objid and 5 other fieldsHigh correlation
mjd is highly correlated with objid and 5 other fieldsHigh correlation

Reproduction

Analysis started2022-06-30 21:53:12.863952
Analysis finished2022-06-30 21:54:16.241535
Duration1 minute and 3.38 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

objid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct67951
Distinct (%)65.0%
Missing471
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1.2376626 × 1018
Minimum1.237645942 × 1018
Maximum1.237680531 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:16.406962image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.237645942 × 1018
5-th percentile1.237650763 × 1018
Q11.237657613 × 1018
median1.237662237 × 1018
Q31.237667211 × 1018
95-th percentile1.2376786 × 1018
Maximum1.237680531 × 1018
Range3.458899502 × 1013
Interquartile range (IQR)9.59764208 × 1012

Descriptive statistics

Standard deviation7.297186796 × 1012
Coefficient of variation (CV)5.895941911 × 10-6
Kurtosis-0.100111674
Mean1.2376626 × 1018
Median Absolute Deviation (MAD)4.62837783 × 1012
Skewness0.2892833877
Sum1.293716339 × 1023
Variance5.324893513 × 1025
MonotonicityNot monotonic
2022-06-30T23:54:16.673742image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.237657611 × 101822
 
< 0.1%
1.237665549 × 101818
 
< 0.1%
1.237659326 × 101818
 
< 0.1%
1.237666338 × 101817
 
< 0.1%
1.237661384 × 101817
 
< 0.1%
1.237656906 × 101817
 
< 0.1%
1.237659326 × 101816
 
< 0.1%
1.237666338 × 101816
 
< 0.1%
1.237648703 × 101815
 
< 0.1%
1.237655743 × 101813
 
< 0.1%
Other values (67941)104360
99.4%
(Missing)471
 
0.4%
ValueCountFrequency (%)
1.237645942 × 10181
 
< 0.1%
1.237645943 × 10181
 
< 0.1%
1.237645943 × 10181
 
< 0.1%
1.237645943 × 10182
< 0.1%
1.237645943 × 10181
 
< 0.1%
1.237645944 × 10182
< 0.1%
1.237645944 × 10181
 
< 0.1%
1.237645944 × 10181
 
< 0.1%
1.237645944 × 10181
 
< 0.1%
1.237645944 × 10183
< 0.1%
ValueCountFrequency (%)
1.237680531 × 10181
 
< 0.1%
1.237680531 × 10182
< 0.1%
1.237680531 × 10182
< 0.1%
1.237680531 × 10182
< 0.1%
1.237680531 × 10184
< 0.1%
1.23768053 × 10181
 
< 0.1%
1.23768053 × 10181
 
< 0.1%
1.23768053 × 10181
 
< 0.1%
1.23768053 × 10181
 
< 0.1%
1.23768053 × 10181
 
< 0.1%

ra
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99998
Distinct (%)95.7%
Missing503
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean177.3636281
Minimum0.0130606182
Maximum359.9996152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:16.862804image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.0130606182
5-th percentile19.29229108
Q1136.2073294
median180.3154659
Q3224.3855998
95-th percentile331.8527005
Maximum359.9996152
Range359.9865546
Interquartile range (IQR)88.17827047

Descriptive statistics

Standard deviation78.32375233
Coefficient of variation (CV)0.4415998542
Kurtosis0.2894332768
Mean177.3636281
Median Absolute Deviation (MAD)44.08668798
Skewness-0.1478046655
Sum18533967.05
Variance6134.610179
MonotonicityNot monotonic
2022-06-30T23:54:17.053960image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135.73130136
 
< 0.1%
0.38487055365
 
< 0.1%
310.52356375
 
< 0.1%
181.28812195
 
< 0.1%
240.27672255
 
< 0.1%
169.62617595
 
< 0.1%
132.72642255
 
< 0.1%
3.4913973415
 
< 0.1%
355.71719844
 
< 0.1%
329.9542084
 
< 0.1%
Other values (99988)104448
99.5%
(Missing)503
 
0.5%
ValueCountFrequency (%)
0.01306061821
< 0.1%
0.0138900331
< 0.1%
0.01517668851
< 0.1%
0.016392431
< 0.1%
0.01936032761
< 0.1%
0.0298381741
< 0.1%
0.03022144781
< 0.1%
0.03146774941
< 0.1%
0.038054381
< 0.1%
0.03927184171
< 0.1%
ValueCountFrequency (%)
359.99961521
 
< 0.1%
359.98185231
 
< 0.1%
359.97393351
 
< 0.1%
359.97306151
 
< 0.1%
359.9726753
< 0.1%
359.96440621
 
< 0.1%
359.9612131
 
< 0.1%
359.95373661
 
< 0.1%
359.94896871
 
< 0.1%
359.94814661
 
< 0.1%

dec
Real number (ℝ)

HIGH CORRELATION

Distinct99998
Distinct (%)95.7%
Missing514
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean25.07701865
Minimum-19.49545595
Maximum84.49049355
Zeros0
Zeros (%)0.0%
Negative12314
Negative (%)11.7%
Memory size820.4 KiB
2022-06-30T23:54:17.311384image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-19.49545595
5-th percentile-2.564156087
Q16.796899074
median23.97616554
Q340.35628529
95-th percentile59.54625483
Maximum84.49049355
Range103.9859495
Interquartile range (IQR)33.55938621

Descriptive statistics

Standard deviation20.54955798
Coefficient of variation (CV)0.8194577777
Kurtosis-0.9006547811
Mean25.07701865
Median Absolute Deviation (MAD)16.77616393
Skewness0.2645675607
Sum2620197.371
Variance422.284333
MonotonicityNot monotonic
2022-06-30T23:54:17.486040image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.422044676
 
< 0.1%
40.431679045
 
< 0.1%
7.1347546265
 
< 0.1%
4.6764975565
 
< 0.1%
21.226724745
 
< 0.1%
27.292418455
 
< 0.1%
17.466694945
 
< 0.1%
32.903710064
 
< 0.1%
44.85829334
 
< 0.1%
3.8810938994
 
< 0.1%
Other values (99988)104438
99.5%
(Missing)514
 
0.5%
ValueCountFrequency (%)
-19.495455951
< 0.1%
-19.364576461
< 0.1%
-19.294770921
< 0.1%
-19.252508631
< 0.1%
-19.052569321
< 0.1%
-19.045695381
< 0.1%
-19.008698621
< 0.1%
-18.96405791
< 0.1%
-18.90487261
< 0.1%
-18.865960691
< 0.1%
ValueCountFrequency (%)
84.490493551
< 0.1%
84.415659611
< 0.1%
84.393791341
< 0.1%
84.375067761
< 0.1%
84.355133661
< 0.1%
84.343709671
< 0.1%
84.334910611
< 0.1%
84.333475411
< 0.1%
84.317067481
< 0.1%
84.317064071
< 0.1%

u
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct79840
Distinct (%)76.4%
Missing453
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean18.64612461
Minimum10.61181
Maximum19.59995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:17.681342image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum10.61181
5-th percentile16.994123
Q118.226545
median18.88153
Q319.2784
95-th percentile19.536647
Maximum19.59995
Range8.98814
Interquartile range (IQR)1.051855

Descriptive statistics

Standard deviation0.8286945168
Coefficient of variation (CV)0.04444325748
Kurtosis2.614982487
Mean18.64612461
Median Absolute Deviation (MAD)0.46885
Skewness-1.410525129
Sum1949396.389
Variance0.6867346022
MonotonicityNot monotonic
2022-06-30T23:54:17.842540image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.282328
 
< 0.1%
19.500517
 
< 0.1%
18.56097
 
< 0.1%
19.56327
 
< 0.1%
19.065287
 
< 0.1%
18.9357
 
< 0.1%
18.979737
 
< 0.1%
19.293487
 
< 0.1%
19.175887
 
< 0.1%
19.463347
 
< 0.1%
Other values (79830)104476
99.5%
(Missing)453
 
0.4%
ValueCountFrequency (%)
10.611811
< 0.1%
10.996231
< 0.1%
11.491251
< 0.1%
11.507941
< 0.1%
11.716391
< 0.1%
11.845771
< 0.1%
11.960911
< 0.1%
12.055211
< 0.1%
12.101681
< 0.1%
12.109331
< 0.1%
ValueCountFrequency (%)
19.599951
 
< 0.1%
19.599941
 
< 0.1%
19.599931
 
< 0.1%
19.599923
< 0.1%
19.59991
 
< 0.1%
19.599892
< 0.1%
19.599872
< 0.1%
19.599831
 
< 0.1%
19.599821
 
< 0.1%
19.599812
< 0.1%

g
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct85895
Distinct (%)82.2%
Missing498
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean17.44646107
Minimum9.668339
Maximum19.99605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:18.014488image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9.668339
5-th percentile15.6636305
Q116.876755
median17.543095
Q318.09984
95-th percentile19.0453195
Maximum19.99605
Range10.327711
Interquartile range (IQR)1.223085

Descriptive statistics

Standard deviation1.003252479
Coefficient of variation (CV)0.05750464093
Kurtosis1.647466801
Mean17.44646107
Median Absolute Deviation (MAD)0.599615
Skewness-0.7048097714
Sum1823190.075
Variance1.006515537
MonotonicityNot monotonic
2022-06-30T23:54:18.635163image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.002617
 
< 0.1%
19.361846
 
< 0.1%
17.705186
 
< 0.1%
17.628196
 
< 0.1%
18.333416
 
< 0.1%
18.617526
 
< 0.1%
18.064626
 
< 0.1%
18.784486
 
< 0.1%
17.826195
 
< 0.1%
19.150255
 
< 0.1%
Other values (85885)104443
99.5%
(Missing)498
 
0.5%
ValueCountFrequency (%)
9.6683391
< 0.1%
9.9881
< 0.1%
9.9890081
< 0.1%
10.03481
< 0.1%
10.09851
< 0.1%
10.390041
< 0.1%
10.49821
< 0.1%
10.511391
< 0.1%
10.53111
< 0.1%
10.533531
< 0.1%
ValueCountFrequency (%)
19.996051
< 0.1%
19.98421
< 0.1%
19.974991
< 0.1%
19.90731
< 0.1%
19.906421
< 0.1%
19.894741
< 0.1%
19.877251
< 0.1%
19.868321
< 0.1%
19.863731
< 0.1%
19.861681
< 0.1%

r
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct87969
Distinct (%)84.2%
Missing536
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean16.93342247
Minimum9.005167
Maximum31.9901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:18.802933image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9.005167
5-th percentile15.060213
Q116.220455
median16.93446
Q317.648455
95-th percentile18.928511
Maximum31.9901
Range22.984933
Interquartile range (IQR)1.428

Descriptive statistics

Standard deviation1.158454844
Coefficient of variation (CV)0.0684123275
Kurtosis2.035794
Mean16.93342247
Median Absolute Deviation (MAD)0.714005
Skewness-0.1402745977
Sum1768933.045
Variance1.342017625
MonotonicityNot monotonic
2022-06-30T23:54:18.954827image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.365916
 
< 0.1%
19.235136
 
< 0.1%
18.181696
 
< 0.1%
18.799916
 
< 0.1%
18.887915
 
< 0.1%
17.186945
 
< 0.1%
17.418425
 
< 0.1%
17.034125
 
< 0.1%
18.338455
 
< 0.1%
18.488275
 
< 0.1%
Other values (87959)104410
99.4%
(Missing)536
 
0.5%
ValueCountFrequency (%)
9.0051671
< 0.1%
9.0504941
< 0.1%
9.1675261
< 0.1%
9.3562781
< 0.1%
9.5333391
< 0.1%
9.5646291
< 0.1%
9.6032181
< 0.1%
9.6857971
< 0.1%
9.6905821
< 0.1%
9.731441
< 0.1%
ValueCountFrequency (%)
31.99011
< 0.1%
29.571861
< 0.1%
28.94221
< 0.1%
28.203841
< 0.1%
27.063851
< 0.1%
26.792651
< 0.1%
26.591331
< 0.1%
26.305791
< 0.1%
25.496121
< 0.1%
24.97271
< 0.1%

i
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct88623
Distinct (%)84.8%
Missing506
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean16.68246364
Minimum8.848403
Maximum32.14147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:19.184036image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum8.848403
5-th percentile14.751799
Q115.889685
median16.64607
Q317.41619
95-th percentile18.8476525
Maximum32.14147
Range23.293067
Interquartile range (IQR)1.526505

Descriptive statistics

Standard deviation1.23673037
Coefficient of variation (CV)0.07413355708
Kurtosis1.777398393
Mean16.68246364
Median Absolute Deviation (MAD)0.76327
Skewness0.03995694952
Sum1743217.355
Variance1.529502008
MonotonicityNot monotonic
2022-06-30T23:54:19.408363image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.241536
 
< 0.1%
19.021796
 
< 0.1%
17.376136
 
< 0.1%
18.774546
 
< 0.1%
18.133226
 
< 0.1%
18.576725
 
< 0.1%
15.994265
 
< 0.1%
18.17925
 
< 0.1%
18.76315
 
< 0.1%
18.991875
 
< 0.1%
Other values (88613)104439
99.5%
(Missing)506
 
0.5%
ValueCountFrequency (%)
8.8484031
< 0.1%
8.8966961
< 0.1%
8.9174891
< 0.1%
9.0824331
< 0.1%
9.100111
< 0.1%
9.3212351
< 0.1%
9.4009041
< 0.1%
9.4068951
< 0.1%
9.4563141
< 0.1%
9.462081
< 0.1%
ValueCountFrequency (%)
32.141471
< 0.1%
32.101781
< 0.1%
30.163591
< 0.1%
27.471431
< 0.1%
26.96871
< 0.1%
26.893991
< 0.1%
26.496361
< 0.1%
26.04411
< 0.1%
25.153621
< 0.1%
24.877111
< 0.1%

z
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89451
Distinct (%)85.6%
Missing518
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean16.52722576
Minimum8.947795
Maximum29.38374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:19.607547image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum8.947795
5-th percentile14.505378
Q115.6484
median16.479285
Q317.3203725
95-th percentile18.82262
Maximum29.38374
Range20.435945
Interquartile range (IQR)1.6719725

Descriptive statistics

Standard deviation1.308750962
Coefficient of variation (CV)0.07918757695
Kurtosis0.8258176243
Mean16.52722576
Median Absolute Deviation (MAD)0.835415
Skewness0.1071717878
Sum1726797.602
Variance1.71282908
MonotonicityNot monotonic
2022-06-30T23:54:19.840909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.264166
 
< 0.1%
18.615936
 
< 0.1%
19.055165
 
< 0.1%
16.367915
 
< 0.1%
19.08075
 
< 0.1%
19.083075
 
< 0.1%
18.383365
 
< 0.1%
15.653325
 
< 0.1%
18.60765
 
< 0.1%
15.512775
 
< 0.1%
Other values (89441)104430
99.5%
(Missing)518
 
0.5%
ValueCountFrequency (%)
8.9477951
< 0.1%
9.1148251
< 0.1%
9.2288361
< 0.1%
9.5370691
< 0.1%
9.6123331
< 0.1%
9.7419121
< 0.1%
9.7780911
< 0.1%
9.8366411
< 0.1%
9.8938561
< 0.1%
9.9181731
< 0.1%
ValueCountFrequency (%)
29.383741
< 0.1%
28.790551
< 0.1%
28.026491
< 0.1%
27.673361
< 0.1%
26.513541
< 0.1%
26.046611
< 0.1%
25.333641
< 0.1%
24.704311
< 0.1%
24.682151
< 0.1%
24.564981
< 0.1%

run
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct516
Distinct (%)0.5%
Missing508
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean3987.288836
Minimum109
Maximum8162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:20.036728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile1231
Q12826
median3903
Q35061
95-th percentile7713
Maximum8162
Range8053
Interquartile range (IQR)2235

Descriptive statistics

Standard deviation1698.666908
Coefficient of variation (CV)0.4260205313
Kurtosis-0.09943297995
Mean3987.288836
Median Absolute Deviation (MAD)1078
Skewness0.2897279982
Sum416639785
Variance2885469.265
MonotonicityNot monotonic
2022-06-30T23:54:20.285733image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22433116
 
3.0%
38932422
 
2.3%
37042221
 
2.1%
29862166
 
2.1%
7522112
 
2.0%
59722103
 
2.0%
46741850
 
1.8%
45761618
 
1.5%
42941575
 
1.5%
37051536
 
1.5%
Other values (506)83773
79.8%
ValueCountFrequency (%)
10916
 
< 0.1%
21143
 
< 0.1%
2593
 
< 0.1%
2872
 
< 0.1%
30786
 
0.1%
30848
 
< 0.1%
745113
 
0.1%
7522112
2.0%
756874
0.8%
99415
 
< 0.1%
ValueCountFrequency (%)
816228
 
< 0.1%
81582
 
< 0.1%
815711
 
< 0.1%
815632
< 0.1%
815513
 
< 0.1%
815114
 
< 0.1%
81505
 
< 0.1%
81492
 
< 0.1%
81421
 
< 0.1%
811670
0.1%

rerun
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing539
Missing (%)0.5%
Memory size820.4 KiB
301.0
104461 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters522305
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row301.0
2nd row301.0
3rd row301.0
4th row301.0
5th row301.0

Common Values

ValueCountFrequency (%)
301.0104461
99.5%
(Missing)539
 
0.5%

Length

2022-06-30T23:54:20.513403image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-30T23:54:20.695925image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
301.0104461
100.0%

Most occurring characters

ValueCountFrequency (%)
0208922
40.0%
3104461
20.0%
1104461
20.0%
.104461
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number417844
80.0%
Other Punctuation104461
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0208922
50.0%
3104461
25.0%
1104461
25.0%
Other Punctuation
ValueCountFrequency (%)
.104461
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common522305
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0208922
40.0%
3104461
20.0%
1104461
20.0%
.104461
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII522305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0208922
40.0%
3104461
20.0%
1104461
20.0%
.104461
20.0%

camcol
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing496
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean3.274353135
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:20.780520image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.620960607
Coefficient of variation (CV)0.4950475835
Kurtosis-1.141387787
Mean3.274353135
Median Absolute Deviation (MAD)1
Skewness0.1268195353
Sum342183
Variance2.627513289
MonotonicityNot monotonic
2022-06-30T23:54:20.890432image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
419393
18.5%
319318
18.4%
119149
18.2%
219037
18.1%
516208
15.4%
611399
10.9%
(Missing)496
 
0.5%
ValueCountFrequency (%)
119149
18.2%
219037
18.1%
319318
18.4%
419393
18.5%
516208
15.4%
611399
10.9%
ValueCountFrequency (%)
611399
10.9%
516208
15.4%
419393
18.5%
319318
18.4%
219037
18.1%
119149
18.2%

field
Real number (ℝ≥0)

HIGH CORRELATION

Distinct840
Distinct (%)0.8%
Missing476
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean187.085808
Minimum11
Maximum982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:21.027326image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile31
Q185
median153
Q3249
95-th percentile476
Maximum982
Range971
Interquartile range (IQR)164

Descriptive statistics

Standard deviation140.884349
Coefficient of variation (CV)0.7530466927
Kurtosis2.616465326
Mean187.085808
Median Absolute Deviation (MAD)77
Skewness1.494813155
Sum19554957
Variance19848.39979
MonotonicityNot monotonic
2022-06-30T23:54:21.255410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98463
 
0.4%
61462
 
0.4%
99455
 
0.4%
65450
 
0.4%
67448
 
0.4%
63445
 
0.4%
97444
 
0.4%
66439
 
0.4%
56438
 
0.4%
131432
 
0.4%
Other values (830)100048
95.3%
(Missing)476
 
0.5%
ValueCountFrequency (%)
11215
0.2%
12188
0.2%
13227
0.2%
14211
0.2%
15192
0.2%
16209
0.2%
17222
0.2%
18241
0.2%
19221
0.2%
20248
0.2%
ValueCountFrequency (%)
9821
< 0.1%
9771
< 0.1%
9741
< 0.1%
9721
< 0.1%
9711
< 0.1%
9701
< 0.1%
8601
< 0.1%
8481
< 0.1%
8461
< 0.1%
8452
< 0.1%

specobjid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct100000
Distinct (%)95.7%
Missing498
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.968231684 × 1018
Minimum2.994896774 × 1017
Maximum1.317645238 × 1019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:21.466406image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2.994896774 × 1017
5-th percentile4.469861826 × 1017
Q11.339866632 × 1018
median2.367807557 × 1018
Q33.30910642 × 1018
95-th percentile8.715861478 × 1018
Maximum1.317645238 × 1019
Range1.287696271 × 1019
Interquartile range (IQR)1.969239787 × 1018

Descriptive statistics

Standard deviation2.552331925 × 1018
Coefficient of variation (CV)0.8598829863
Kurtosis2.949536078
Mean2.968231684 × 1018
Median Absolute Deviation (MAD)9.796265337 × 1017
Skewness1.793483855
Sum3.101861474 × 1023
Variance6.514398253 × 1036
MonotonicityNot monotonic
2022-06-30T23:54:21.657630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.446692163 × 10186
 
< 0.1%
7.202432938 × 10186
 
< 0.1%
1.34212606 × 10186
 
< 0.1%
1.621351993 × 10185
 
< 0.1%
2.921850791 × 10185
 
< 0.1%
1.104539735 × 10185
 
< 0.1%
8.551262136 × 10185
 
< 0.1%
1.375940167 × 10184
 
< 0.1%
4.255045766 × 10184
 
< 0.1%
8.549063663 × 10184
 
< 0.1%
Other values (99990)104452
99.5%
(Missing)498
 
0.5%
ValueCountFrequency (%)
2.994896774 × 10171
< 0.1%
2.995190894 × 10171
< 0.1%
2.995278855 × 10171
< 0.1%
2.995669181 × 10172
< 0.1%
2.995935813 × 10171
< 0.1%
2.996007281 × 10171
< 0.1%
2.996048513 × 10171
< 0.1%
2.996169459 × 10171
< 0.1%
2.996177705 × 10171
< 0.1%
2.996375618 × 10171
< 0.1%
ValueCountFrequency (%)
1.317645238 × 10192
< 0.1%
1.314852314 × 10191
< 0.1%
1.314739421 × 10191
< 0.1%
1.314731203 × 10191
< 0.1%
1.314720015 × 10191
< 0.1%
1.314717266 × 10191
< 0.1%
1.314625265 × 10191
< 0.1%
1.314619217 × 10191
< 0.1%
1.314616001 × 10191
< 0.1%
1.314614324 × 10191
< 0.1%

class
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size820.4 KiB
GALAXY
51323 
STAR
39296 
QSO
14381 

Length

Max length6
Median length4
Mean length4.840619048
Min length3

Characters and Unicode

Total characters508265
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTAR
2nd rowGALAXY
3rd rowGALAXY
4th rowGALAXY
5th rowQSO

Common Values

ValueCountFrequency (%)
GALAXY51323
48.9%
STAR39296
37.4%
QSO14381
 
13.7%

Length

2022-06-30T23:54:21.876038image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-30T23:54:22.012174image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
galaxy51323
48.9%
star39296
37.4%
qso14381
 
13.7%

Most occurring characters

ValueCountFrequency (%)
A141942
27.9%
S53677
 
10.6%
G51323
 
10.1%
L51323
 
10.1%
X51323
 
10.1%
Y51323
 
10.1%
T39296
 
7.7%
R39296
 
7.7%
Q14381
 
2.8%
O14381
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter508265
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A141942
27.9%
S53677
 
10.6%
G51323
 
10.1%
L51323
 
10.1%
X51323
 
10.1%
Y51323
 
10.1%
T39296
 
7.7%
R39296
 
7.7%
Q14381
 
2.8%
O14381
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin508265
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A141942
27.9%
S53677
 
10.6%
G51323
 
10.1%
L51323
 
10.1%
X51323
 
10.1%
Y51323
 
10.1%
T39296
 
7.7%
R39296
 
7.7%
Q14381
 
2.8%
O14381
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII508265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A141942
27.9%
S53677
 
10.6%
G51323
 
10.1%
L51323
 
10.1%
X51323
 
10.1%
Y51323
 
10.1%
T39296
 
7.7%
R39296
 
7.7%
Q14381
 
2.8%
O14381
 
2.8%

redshift
Real number (ℝ)

HIGH CORRELATION

Distinct99298
Distinct (%)95.0%
Missing517
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.2024597147
Minimum-0.004136078
Maximum7.011245
Zeros401
Zeros (%)0.4%
Negative25265
Negative (%)24.1%
Memory size820.4 KiB
2022-06-30T23:54:22.143921image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-0.004136078
5-th percentile-0.00050897616
Q17.4106295 × 10-6
median0.0483957
Q30.101922
95-th percentile1.454975
Maximum7.011245
Range7.015381078
Interquartile range (IQR)0.1019145894

Descriptive statistics

Standard deviation0.4840987539
Coefficient of variation (CV)2.391086813
Kurtosis16.35971385
Mean0.2024597147
Median Absolute Deviation (MAD)0.04840843535
Skewness3.552813238
Sum21153.59837
Variance0.2343516035
MonotonicityNot monotonic
2022-06-30T23:54:22.311392image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0401
 
0.4%
0.00415325413
 
< 0.1%
0.00046062318
 
< 0.1%
1.307666
 
< 0.1%
0.57387846
 
< 0.1%
1.0364736
 
< 0.1%
0.82407155
 
< 0.1%
0.43898645
 
< 0.1%
0.23541265
 
< 0.1%
0.15481145
 
< 0.1%
Other values (99288)104023
99.1%
(Missing)517
 
0.5%
ValueCountFrequency (%)
-0.0041360783
< 0.1%
-0.0040206561
 
< 0.1%
-0.0040205891
 
< 0.1%
-0.0040197341
 
< 0.1%
-0.0039559371
 
< 0.1%
-0.003921771
 
< 0.1%
-0.0038081791
 
< 0.1%
-0.0037981161
 
< 0.1%
-0.0037899811
 
< 0.1%
-0.0037301391
 
< 0.1%
ValueCountFrequency (%)
7.0112451
< 0.1%
6.9911772
< 0.1%
6.5717131
< 0.1%
6.5176351
< 0.1%
6.4042331
< 0.1%
6.3764961
< 0.1%
6.3545541
< 0.1%
6.2692511
< 0.1%
6.1276841
< 0.1%
5.9084461
< 0.1%

plate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6117
Distinct (%)5.9%
Missing510
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2636.85456
Minimum266
Maximum11703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:22.529537image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum266
5-th percentile397
Q11190
median2103
Q32939
95-th percentile7749
Maximum11703
Range11437
Interquartile range (IQR)1749

Descriptive statistics

Standard deviation2268.470352
Coefficient of variation (CV)0.8602940739
Kurtosis2.95245923
Mean2636.85456
Median Absolute Deviation (MAD)870
Skewness1.794538261
Sum275524933
Variance5145957.737
MonotonicityNot monotonic
2022-06-30T23:54:22.730495image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2939238
 
0.2%
2337216
 
0.2%
2299204
 
0.2%
2888202
 
0.2%
2394197
 
0.2%
1596173
 
0.2%
2310169
 
0.2%
2384161
 
0.2%
1433161
 
0.2%
2667161
 
0.2%
Other values (6107)102608
97.7%
(Missing)510
 
0.5%
ValueCountFrequency (%)
26617
< 0.1%
26722
< 0.1%
26818
< 0.1%
26913
< 0.1%
27022
< 0.1%
27118
< 0.1%
27211
< 0.1%
27316
< 0.1%
2749
< 0.1%
2755
 
< 0.1%
ValueCountFrequency (%)
117032
 
< 0.1%
116781
 
< 0.1%
116774
< 0.1%
116768
< 0.1%
116549
< 0.1%
116534
< 0.1%
116516
< 0.1%
116504
< 0.1%
116491
 
< 0.1%
116363
 
< 0.1%

mjd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2167
Distinct (%)2.1%
Missing503
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean53943.28491
Minimum51608
Maximum58543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:22.902598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum51608
5-th percentile51957
Q152734
median53734
Q354613
95-th percentile57357
Maximum58543
Range6935
Interquartile range (IQR)1879

Descriptive statistics

Standard deviation1575.078088
Coefficient of variation (CV)0.02919877963
Kurtosis0.4103413998
Mean53943.28491
Median Absolute Deviation (MAD)972
Skewness0.9230120903
Sum5636911443
Variance2480870.984
MonotonicityNot monotonic
2022-06-30T23:54:23.074607image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52411431
 
0.4%
53084426
 
0.4%
53770382
 
0.4%
53050360
 
0.3%
52781347
 
0.3%
52378335
 
0.3%
52765329
 
0.3%
53762326
 
0.3%
54169321
 
0.3%
52641319
 
0.3%
Other values (2157)100921
96.1%
(Missing)503
 
0.5%
ValueCountFrequency (%)
5160822
 
< 0.1%
51609101
0.1%
5161217
 
< 0.1%
5161359
0.1%
5161412
 
< 0.1%
5161570
0.1%
5163017
 
< 0.1%
5163318
 
< 0.1%
5163748
< 0.1%
5165821
 
< 0.1%
ValueCountFrequency (%)
5854314
< 0.1%
5852613
< 0.1%
585238
 
< 0.1%
585227
 
< 0.1%
5851520
< 0.1%
5851412
< 0.1%
5851214
< 0.1%
5851111
 
< 0.1%
5851029
< 0.1%
585091
 
< 0.1%

fiberid
Real number (ℝ≥0)

Distinct1000
Distinct (%)1.0%
Missing517
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean343.3096293
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.4 KiB
2022-06-30T23:54:23.248544image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q1161
median328
Q3504
95-th percentile709
Maximum1000
Range999
Interquartile range (IQR)343

Descriptive statistics

Standard deviation218.4626466
Coefficient of variation (CV)0.6363429044
Kurtosis-0.2271837203
Mean343.3096293
Median Absolute Deviation (MAD)172
Skewness0.4810569027
Sum35870020
Variance47725.92797
MonotonicityNot monotonic
2022-06-30T23:54:23.475091image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46216
 
0.2%
250209
 
0.2%
90208
 
0.2%
210207
 
0.2%
48207
 
0.2%
44205
 
0.2%
134203
 
0.2%
52199
 
0.2%
529194
 
0.2%
124193
 
0.2%
Other values (990)102442
97.6%
(Missing)517
 
0.5%
ValueCountFrequency (%)
1150
0.1%
2174
0.2%
3151
0.1%
4182
0.2%
5160
0.2%
6179
0.2%
7174
0.2%
8183
0.2%
9188
0.2%
10190
0.2%
ValueCountFrequency (%)
100030
< 0.1%
9998
 
< 0.1%
99818
< 0.1%
99720
< 0.1%
99626
< 0.1%
9959
 
< 0.1%
99429
< 0.1%
9939
 
< 0.1%
99230
< 0.1%
99112
 
< 0.1%

Interactions

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2022-06-30T23:53:44.945399image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-06-30T23:53:49.735320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:52.334975image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:54.940622image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:57.289708image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:00.182620image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:03.984827image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:06.928694image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:09.835701image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:13.118609image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:32.857112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:35.634159image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:37.955116image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:40.316207image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:42.756148image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:45.088396image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:47.475174image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:49.876971image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:52.492228image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:55.081757image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:57.446363image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:00.569252image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:04.144477image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:07.074694image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:10.084359image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:13.335875image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:33.048153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:35.781161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:38.093414image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:40.450204image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:42.903195image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:45.227589image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:47.616185image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:50.065377image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:52.633363image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:55.222903image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:57.571777image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:00.994613image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:04.316067image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:07.248662image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:10.265857image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:13.504819image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:33.209110image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:35.967111image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:38.231412image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:40.585165image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:43.051151image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:45.370583image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:47.748834image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:50.237734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:52.806264image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:55.366913image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:53:57.728461image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:01.294497image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:04.473616image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:07.414815image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-06-30T23:54:10.456228image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-06-30T23:54:23.702685image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-30T23:54:23.970425image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-30T23:54:24.271842image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-30T23:54:24.572617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-30T23:54:24.730557image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-30T23:54:13.798696image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-30T23:54:14.398032image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-30T23:54:15.246809image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-30T23:54:15.872886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

objidradecugrizrunreruncamcolfieldspecobjidclassredshiftplatemjdfiberid
01.237658e+18122.47253629.47754618.8010418.7940919.1790419.4689319.621212830.0301.01.082.05.011614e+18STAR0.0001134451.055537.0848.0
11.237661e+18157.22049138.04446219.2163417.3645016.3922215.9616015.624903647.0301.01.0123.01.607801e+18GALAXY0.1070831428.052998.056.0
21.237665e+18128.42649922.24418717.5828516.6668316.3751116.2357316.032834517.0301.02.0113.02.171901e+18GALAXY0.0247771929.053349.0144.0
31.237655e+18148.3119103.60364919.4933918.0261117.2192416.7386816.434142125.0301.03.0197.06.429279e+17GALAXY0.091564571.052286.0142.0
41.237649e+18188.6159060.96650119.1178718.8089218.6538618.4303718.44886752.0301.06.0301.03.266113e+17QSO1.528742290.051941.0365.0
51.237651e+18172.252999-3.63829119.1144118.7914918.6316118.7004518.595551231.0301.01.020.03.670583e+17QSO0.864346326.052375.054.0
61.237671e+18191.230726-3.11274119.4139918.2346317.7426317.5867617.506226005.0301.04.0143.03.289917e+18STAR-0.0001512922.054612.0135.0
71.237649e+18239.6012560.69235218.9647817.7532717.1384116.7557716.49653745.0301.05.0540.03.863387e+17GALAXY0.101707343.051692.0564.0
81.237666e+18210.12981821.88738118.9725417.9522717.5522217.2579617.233624678.0301.01.062.03.118898e+18GALAXY0.0668142770.054510.0565.0
91.237665e+18215.32791334.26115718.5417717.2024916.5870316.1519815.968994512.0301.05.0112.02.071777e+18GALAXY0.0682311840.053472.0442.0

Last rows

objidradecugrizrunreruncamcolfieldspecobjidclassredshiftplatemjdfiberid
1049901.237662e+18224.01189536.24303518.4463116.8254816.0756515.6712515.417763893.0301.05.0379.01.558349e+18GALAXY0.0472481384.053121.0378.0
1049911.237672e+18259.92716234.80060418.3255716.8025116.0217915.6250515.332176161.0301.06.0102.05.622990e+18GALAXY0.0836484994.055739.0894.0
1049921.237661e+18139.31365031.77284617.4571316.3293215.9616115.7617815.836753704.0301.02.052.02.680879e+18STAR0.0000302381.053762.0403.0
1049931.237661e+18140.91423136.97257417.3423416.2390516.1727016.1883116.220203606.0301.06.0119.01.434510e+18STAR-0.0000611274.052995.0413.0
1049941.237671e+1872.49090721.88920318.6820017.3559816.7803716.5307016.393746003.0301.02.0201.03.038955e+18STAR0.0000522699.054414.0549.0
1049951.237666e+1853.1046460.40620619.5617018.1029517.5307617.2246517.074874849.0301.04.0809.04.673661e+17GALAXY0.022364415.051810.0428.0
1049961.237667e+18139.37113221.87120919.2014517.6739717.0959416.8894516.817905071.0301.03.0169.02.594136e+18STAR0.0000772304.053762.0227.0
1049971.237658e+18195.61643350.15511917.3119416.2106415.8369015.7082215.676142964.0301.05.0367.03.646903e+18STAR-0.0001213239.054888.0410.0
1049981.237662e+18185.5850526.43450118.7371317.5977017.0156416.6791416.428063841.0301.01.0138.01.830781e+18GALAXY0.0749011626.053472.0248.0
1049991.237652e+18153.67299564.45556119.1155718.8461718.6906318.4821018.460871412.0301.04.082.05.495722e+17QSO1.536277488.051914.0484.0

Duplicate rows

Most frequently occurring

objidradecugrizrunreruncamcolfieldspecobjidclassredshiftplatemjdfiberid# duplicates
211.237650e+1860.006771-5.45376518.5352018.4966918.1290018.0837018.072911045.0301.04.0173.05.225630e+17QSO1.381268464.051908.0529.03
941.237655e+18148.8651134.06999519.2137019.0595718.9578518.8979118.965342125.0301.04.0201.06.430538e+17QSO1.436372571.052286.0600.03
1031.237655e+18200.8318074.28825719.3972918.8637918.6923618.7144118.558732190.0301.02.0126.05.360644e+18QSO2.3253784761.055633.0854.03
2501.237659e+18220.91833555.25893219.0329418.9769718.6996918.6131518.648263225.0301.05.081.01.308459e+18QSO1.1591611162.052668.0594.03
2531.237659e+18259.37796526.37445318.9151718.5394818.4412618.3342118.103913225.0301.05.0349.05.632988e+18QSO2.2023985003.055715.0404.03
3011.237661e+18141.40425533.26090619.5199419.3852719.1802319.3294019.233393704.0301.03.067.01.153174e+19QSO1.07924110242.058161.0980.03
3931.237662e+18215.82497248.50436519.0481918.5716818.4882718.2566318.128163918.0301.04.0139.09.495987e+18QSO0.5684418434.057484.0534.03
4081.237662e+18238.85227434.65938619.5240619.2566919.2805219.0816219.192013958.0301.03.063.01.596615e+18QSO1.6253671418.053142.0324.03
4121.237662e+18245.37958230.67334419.5045919.3249119.3365719.2732119.526083958.0301.04.0109.01.229260e+19QSO1.40246010918.058254.0107.03
4131.237662e+18234.51174938.36023619.2431919.0282918.9083818.9027618.215803958.0301.05.030.01.457072e+18QSO0.2878381294.052753.0574.03